Files in this item



application/pdf3223632.pdf (3MB)Restricted to U of Illinois
(no description provided)PDF


Title:Methods for Cluster Analysis and Validation in Microarray Gene Expression Data
Author(s):Kosorukoff, Alexander Lvovich
Doctoral Committee Chair(s):Sylvian Ray
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Biology, Bioinformatics
Abstract:Results. We evaluate this method using both artificial and yeast microarray data. By choosing parameters settings that minimize FCS values and maximize CS values we show major advantages over other clustering methods in particular for identifying combinatorially regulated groups of genes. The results produced provide remarkable enrichment for cis-regulatory elements in clusters of genes known to be regulated by such elements and evidence of extensive combinatorial regulation. Moreover, the method can be generalized when prior information about cis-regulatory sites is absent or it is desirable to calculate FCS values based on functional categorization.
Issue Date:2006
Description:103 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.
Other Identifier(s):(MiAaPQ)AAI3223632
Date Available in IDEALS:2015-09-25
Date Deposited:2006

This item appears in the following Collection(s)

Item Statistics